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The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.
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Physical inactivity is becoming an important threat to public health in today's society. The COVID-19 pandemic has also reduced physical activity (PA) levels given all the restrictions imposed worldwide. In this work, physical activity interventions supported by mobile devices and relying on control engineering principles were proposed. The model was constructed relying on previous studies that consider a fluid analogy of Social Cognitive Theory (SCT), which is a psychological theory that describes how people acquire and maintain certain behaviors, including health-promoting behaviors, through the interplay of personal, environmental, and behavioral factors. The obtained model was validated using secondary data (collected earlier) from a real intervention with a group of male subjects in Great Britain. The present model was extended with new technology for a better understanding of behavior change interventions. This involved the use of applications, such as phone-based ecological momentary assessments, to collect behavioral data and the inclusion of simulations with logical reward conditions for reaching the behavioral threshold. A goal of 10,000 steps per day is recommended due to the significant link observed between higher daily step counts and lower mortality risk. The intervention was designed using a Model Predictive Control (MPC) algorithm configured to obtain a desired performance. The system was tested and validated using simulation scenarios that resemble different situations that may occur in a real setting.
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The use of plastic bottles has become a significant environmental concern, and recycling them has become a priority. Small and medium-sized recycling companies must collect and categorize large volumes of plastic bottles and sell them to larger recycling firms, a process that is time-consuming, costly, and labor-intensive. This manual sorting process can pose health risks, particularly during the COVID-19 pandemic, and can affect worker productivity. To address these issues, this study proposes the development of an automated conveyor belt system that can rapidly and accurately separate plastic bottles by type. The system utilizes an opaque and transparent plastic bottle separation platform, which saves time, cost, and manpower. This system design provides recycling SMEs with a competitive advantage by serving as a practical application model and a prototype with an easy-to-use concept. Key tools employed in this research include product design development (PDD), Kansei engineering, manufacturing process design, controlling system, and fault tree analysis (FTA). The light sensors are critical components in the separation process, detecting the opacity or transparency of the bottles' surfaces. The proposed prototype's reliability will be assessed by FTA, which considers all potential failures. This study contributes to the body of knowledge surrounding the integration of conveyor systems and provides valuable information for businesses seeking to optimize their sorting processes. The guidelines developed in this study can serve as a starting point for further research on the integration of conveyors in waste sorting plants. © 2013 IEEE.
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Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago's "L” stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods' to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that "L” stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users' risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods' access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use.
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A horrifying number of people died because of the COVID-19 pandemic. There was an unexpected threat to food systems, public health, and the workplace. The pandemic has severely disturbed society and there was a serious impediment to the economy. The world went through an unprecedented state of chaos during this period. To avoid anything similar, we can only be cautious. The project aims to develop a web application for the preliminary detection of COVID-19 using Artificial Intelligence(AI). This project would enable faster coordination, secured data storage, and normalized statistics. First, the available chest X-ray datasets were collected and classified as Covid, Non-Covid, and Normal. Then they were trained using various state-of-the-art pre-trained Convolutional Neural Network (CNN) models with the help of Tensor-flow. Further, they were ranked based on their accuracy. The best-performing models were ensembled into a single model to improve the performance. The model with the highest accuracy was transformed into an application programming interface (API) and integrated with the Decentralized application (D-App). The user needs to upload an image of their chest X-ray, and the D-App then suggests if they should take a reverse transcription-polymerase chain reaction (RT-PCR) test for confirmation. © 2022 IEEE.
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SARS-CoV-2 is a new threat to public health due to its increased transmissibility and immune evasion. Angiotensin-converting enzyme 2 (ACE2) plays a critical role in SARS-CoV-2 infection as its serve as the virus's major entry receptor in humans. Vaccines have been authorized for emergency use to control the current pandemic and they have greatly reduced the spread of SARS-CoV-2 and mortality rates, nevertheless this coronavirus has shown the ability to endure crucial mutations that increases its infectivity which makes it likely that the virus will continue to mutate and disseminate. There is a need to find and introduce alternative and effective methods of controlling SARS-CoV-2. Notably, low-level laser therapy (LLLT) is a method of exposing cells or tissue to low levels of red and near infrared light which has a high success rate for treatment of other ailments. The aim of the study is to determine for the first time, the effects of LLLT on SARS-CoV-2 infected HEK293/ACE2 cells and compare them to uninfected ones. Both infected and uninfected HEK293/ACE2 cells were irradiated at a wavelength of 640 nm, at different doses. Then, the effects of laser irradiation on the cells and the virus were evaluated using luciferase, cytotoxicity, and cell viability assays. Preliminary results showed that irradiated uninfected cells had no changes in cell viability and cytotoxicity, while there were changes in irradiated infected cells. In addition, laser irradiation caused cell membrane damage in infected cells. Lastly, uninfected irradiated cells showed no luciferase activity while laser irradiation reduced luciferase activity in infected cells. © 2023 SPIE.
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With the COVID-19 outbreak in 2019, the world is facing a major crisis and people's health is at serious risk. Accurate segmentation of lesions in CT images can help doctors understand disease infections, prescribe the right medicine and control patients' conditions. Fast and accurate diagnosis not only can make the limited medical resources get reasonable allocation, but also can control the spread of disease, and computer-aided diagnosis can achieve this purpose, so this paper proposes a deep learning segmentation network LLDSNet based on a small amount of data, which is divided into two modules: contextual feature-aware module (CFAM) and shape edge detection module (SEDM). Due to the different morphology of lesions in different CT, lesions with dispersion, small lesion area and background area imbalance, lesion area and normal area boundary blurred, etc. The problem of lesion segmentation in COVID-19 poses a major challenge. The CFAM can effectively extract the overall and local features, and the SEDM can accurately find the edges of the lesion area to segment the lesions in this area. The hybrid loss function is used to avoid the class imbalance problem and improve the overall network performance. It is demonstrated that LLDSNet dice achieves 0.696 for a small number of data sets, and the best performance compared to five currently popular segmentation networks. © 2023 SPIE.
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The COVID-19 pandemic is the biggest global health crisis in years. China is the first market primarily affected by the COVID-19 pandemic, with unprecedented lockdown measures bringing real estate and other economic activities to a standstill. This study has two objectives: (1) to identify the risks critical to the risk management of commercial real estate (CRE) development projects based on the project life cycle stages and (2) to identify the stages most affected by the COVID-19 pandemic and the risk factors at different stages. Three rounds of the Delphi study were conducted with nine experts involved in the construction project. The findings indicate that the construction, lease and sale phases are prone to significant risks. Additionally, the analytic hierarchy process (AHP) identified ‘health and safety risk' as the most critical risk factor during the construction phase and ‘marketing and payback risk' as the most critical risk factor during the lease and sale phase. This study enhanced the effectiveness of risk management practices for implementing CRE development projects in China.
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When it comes to supplying oxygen, current standard hospitals in Iran have proven inadequate in the face of the COVID-19 pandemic, particularly during infection peaks. Power disruptions drastically reduce the oxygen pressure in hospitals, putting patients' health at risk. The present study is the first to attempt to power an oxygen concentrator with a solar-energy-based system. The HOMER 2.81 package was used for technical–economic–environmental–energy analysis. The most notable aspects of this work include evaluating different available solar trackers, using up-to-date equipment price data and up-to-date inflation rate, considering the temperature effects on solar cell performance, sensitivity analysis for the best scenario, considering pollution penalties, and using a three-time tariff system with price incentives for renewable power. The study has been carried out at Hajar Hospital, Shahrekord, Chaharmahal and Bakhtiari Province, Iran. The study showed that, by supplying 60% of the power demand, the dual-axis solar tracking system offered the highest annual power output (47,478 kWh). Furthermore, generating power at—$0.008/kWh due to selling power to the grid, the vertical-axis tracker was found to be the most economical design. Comparing the configuration with a vertical-axis tracker with the conventional scenario (relying on the power distribution grid), the investment is estimated to be recovered in three years with $234,300 in savings by the end of the 25th year. In the best economic scenario, 6137 kg CO2 is produced, and the analysis revealed the negative impact of a temperature rise on the performance and solar power output.
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Air pollution is one of the major causes of health risks as it leads to widespread disease and death each year. Countries have invested heavily in fighting air pollution, arguably without convincing results. The outbreak of the highly infectious disease COVID-19 in December 2019 has been declared a pandemic and a worldwide health crisis by World Health Organization (WHO). Countries resorted to city lockdowns that sternly curtailed personal mobility and economic activities to control the spread of this deadly coronavirus disease. This paper examines the impact of Covid-19 city lockdowns on air quality. The researchers adopted a comprehensive interpretative document analysis for this study, which guided the careful but rigorous examination of air quality and coronavirus data. This method affirmed the authenticity of the information examined and interpreted in the US, Italy and China, the study areas. The study found that Covid-19 city lockdowns have contributed to a significant improvement in air quality within the first four months of the outbreak of Covid-19. National Aeronautics and Space Administration (NASA) had reported that NO2 concentrations in the study areas had reduced significantly using evidence from their Sentinel-5P instrument. Air quality in Covid-19 cities' lockdowns also improved because of the enforcement of other types of measures enacted to battle the virus. WHO still believes that the amount of NO2 concentration in the atmosphere is still high per their standards and regulations. Based on this, the researchers recommend that governments and other stakeholders put in much effort in terms of legislation to "win the war” against air pollution.
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PurposeMany older adults engage in volunteer activities, drawing meaning and purpose through such efforts. Social distancing restrictions, put in place during Covid-19 surges to reduce the risk of transmission, disrupted older adult volunteers' lives and volunteer experiences. Social distancing measures provide a unique opportunity to explore what happened when the choices around pausing or stopping volunteering were not entirely within the control of older adults. This paper aims to explore the experiences of older adult volunteers as they navigated uncertainties and made difficult decisions around balancing their safety and their desire to continue volunteering.Design/methodology/approachThe authors conducted interviews with 26 community-dwelling older adults, age 50+, who had engaged in volunteer activities for at least 1 h a week prior to the start of the pandemic. The interviews were conducted on the phone or via Zoom. The authors used thematic analysis to help us analyze the data and identify patterns from participants' experiences.FindingsDespite the risk presented by Covid-19, most participants volunteered during the pandemic. They continued some or all of their previous activities with safety-related adjustments, with some seeking new or different opportunities. Participants' discussions highlight the challenges of volunteering during the pandemic and the importance of engagement to their resiliency and subjective well-being.Originality/valueThis paper provides original contributions to understanding how and why older adults volunteered during the Covid-19 pandemic. The social distancing measures provide a novel opportunity to enrich our understanding of the meaningfulness and value of volunteerism to older adults' lives and subjective well-being.
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Debates within urban studies concerning the relationship between urbanisation and infectious disease focus on issues of urban population growth, density, migration and connectivity. However, an effective long-term risk and wellbeing agenda, without which the threat of future pandemics cannot be mitigated, must also take account of demographic forces and changes as critical drivers of transmission and mortality risk within and beyond cities. A better understanding of the dynamics of fertility, mortality and changing age structures – key determinants of urban decline/growth in addition to migration – provides the foundation upon which healthier cities and a healthy global urban system can be developed. The study of how basic demographic attributes and trends are distributed in space and how they interact with risks, including those of infectious disease, must be incorporated as a priority into a post-COVID-19 urban public health agenda. This perspective concurs with recent debates in urban studies emphasising the demographic drivers of urban change. Moreover, it raises critical questions about the microbial and environmental emphasis of much research on the interface of urban health and governance.
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The COVID-19 pandemic has forced people worldwide to modify their daily activities, including travel plans. To help individuals make informed decisions about visiting public places, Cheng [2] first proposed a real-time COVID-19 risk assessment system called RT-CIRAM and implemented prototypes for two U.S. metropolitan locations. The system calculates a COVID-19 risk score and categorizes the risk levels into high, medium, and low, recommends the safe travel destination using the users' location and the specified distance the user is willing to travel, thereby helping users make informed decisions about their travel plans. © 2023 ACM.
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Surgical plume poses a risk to perioperative nurses and the perioperative team as a whole, as well as the operative patient. Surgical plume contains various hazardous components which pose multiple health risks to the perioperative staff who are exposed to it. Further research is required in order to definitively understand the risks to perioperative staff from long-term exposure to surgical plume and to advance current policies and procedures. The current practice standard on surgical plume management from the Australian College of Perioperative Nurses (ACORN) sets out methods of reducing these risks. However, this standard's utility in practice and barriers to its implementation lead to ongoing unnecessary plume exposure. Through adhering to current practice standards and educating perioperative nurses, the risks posed by surgical plume can be mitigated. Thorough education on this topic will empower nurses to advocate for their safety and the safety of their patients, leading to the reduction of surgical plume exposure.
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The purpose of this paper is to capture the direct and indirect effects of COVID-19 emerging risks on construction projects' success in developing countries from the contractors' perspective. To achieve this, we collected data from Iraqi construction industry and conducted the following multistage research methodology: (1) preliminary investigation with four construction contractors to identify the success indicators of construction projects;(2) focus group session with 11 experts to identify the COVID-19 emerging risks for the construction industry;(3) semistructured interviews with seven construction experts to develop several hypotheses on the effect of COVID-19 emerging risks on project success;(4) survey data collection from 99 construction contractors;and (5) development of a structural equation model (SEM) to analyze the effects of COVID-19 emerging risks on project success. The results of the SEM analysis show that financial market (FM)-related risks, supply chain operations (SCO)-related risks, health and safety of construction workforce (HSCW)-related risks, organizational implications (OI)-related risks, and contractual implications (CI)-related risks have a significant impact on construction projects success. CI-related risks have the strongest total effects on project success, followed by OI-related risks, FM-related risks, HSCW-related risks, and SCO-related risks, respectively. The results also emphasis the significant mediation role of CI between COVID-19 emerging risks and project success. This study contributes to the body of knowledge and contracting companies by helping researchers and construction contractors to better understand how the key risk factors emerging from extreme conditions like the COVID-19 pandemic affect construction projects' success and may serve as a guideline for developing effective response strategies. © 2023 American Society of Civil Engineers.
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BACKGROUND: The geographical, cultural, and linguistic proximity between Taiwan and Mainland China has facilitated rapid growth of cross-strait interactions. Both countries have developed online health consultation platforms on the Internet for the public to access healthcare related information. This study examines factors that influence loyalty to a specific online health consultation platform (OHCP) from a cross-strait perspective. METHODS: Based on the Expectation Confirmation Theory and the combined Trust, Perceived Health Risks and Culture, we examine factors that influence loyalty to OHCPs among cross-strait users by investigating the roles of trust, perceived health risks, and culture. Data was collected through a questionnaire survey. RESULTS: The research models used provide a high-power explanation of loyalty to OHCPs. Results generally align with those of previous studies, with the exception of the relationships between Perceived Health Risks and Perceived Usefulness, Perceived Usefulness and Loyalty, Confirmation and Satisfaction, and Trust and Loyalty. In other words, culture may have moderated these relationships. CONCLUSIONS: Findings can help promote OHCPs among cross-strait users to make things easier for patients, and further reduce the load on the emergency department, especially in view of the still ongoing issues related to global outbreak of Coronavirus disease by facilitating early detection of potential cases.
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Infecciones por Coronavirus , Humanos , China , Brotes de Enfermedades , Servicio de Urgencia en Hospital , Derivación y ConsultaRESUMEN
Nowadays, single-use plastic pollution attracts the attention of scholars, policymakers, and practitioners. In addition to personal protective equipment (PPEs) waste during the COVID-19 pandemic, other unpreceded plastic wastes such as packaging from online shopping and food delivery, viruses confirmatory testing, and drinking straws also contributed to pollution and worsened around the globe. This perspective aimed to provide insights into drinking plastic straws as an important source of plastic pollution. Literature searches confirmed that drinking plastic straws, unlike PPEs, have not been researched whether it is an important contributor to pollution or not during the COVID-19 pandemic. Thus, research on the pollution level of this plastic waste and its association with COVID-19 is required. Drinking straw producers and users require adequate strategies and management of this plastic pollution and more widespread rules and regulations to prevent environmental implications and health risks. This study can usefully give highlights for environmentalists, solid waste management experts, policymakers, and governments by describing the environmental impact and raising health risks of drinking plastic straw pollution.
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In Australia, rates of suicide and poor mental health among construction workers were high before the COVID-19 pandemic. "Lock downs”, "restrictions”, "social distancing” and legislative changes responding to the pandemic have likely exacerbated the working conditions that foster poor mental health. This study analyses the way in which workplace health and safety (WHS) is regulated in Australia against the backdrop of existing research relating to the development of Australia's WHS laws, and the state of mental health of those working in Australia's construction industry. This study was conducted using a doctrinal research methodology and utilising legal theory. This study assesses the capacity of the Australian WHS regulatory system to protect construction workers' mental health by examining and interpreting key provisions in Australia's WHS laws. It then uses a regulatory theory, responsive regulation, to explain the inconsistency between the capacity of those laws to safeguard mental health and the very poor state of mental health of Australia's construction workers. The conclusion reached is that there is scope to improve current WHS laws to better protect construction workers' mental health. A recommendation is made, that current WHS laws are changed to prescribe minimum standards of worker mental health, and mandate control measures to minimise and/or eliminate psychosocial risks. It is submitted that these changes in the law will contribute to a changed culture in Australia's construction sector, which is supportive of mentally healthy workplaces and workers. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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Un modèle philanthropique axé sur le développement communautaire serait-il en train de renforcer les politiques coloniales plutôt que d'offrir des bénéfices économiques équitables? Cette étude analyse les transcriptions de vingt webinaires publics sur la philanthropie et la Loi sur les Indiens et évalue les 54 fondations communautaires établis au Manitoba, Canada. Ces 54 fondations servent seulement les villes et municipalités des colons-il n'y en a pas une seule dans les communautés autochtones. Comme elles ne desservent que leurs régions géographiques spécifiques, les fondations communautaires au Manitoba concentrent la richesse dans les villes et municipalités dominées par les colons, accaparant des ressources qui pourraient aider les communautés autochtones. Ce modèle philanthropique, en excluant les communautés les plus pauvres du Manitoba, renforce la marginalisation, la pauvreté et les risques de santé dans les communautés autochtones.Alternate :Could a philanthropic model aimed at community development enforce colonial policy rather than providing equitable economic opportunity? This research analyzes the transcripts of 20 public webinars on philanthropy and the Indian Act and maps the 54 community foundations in Manitoba, Canada. All 54 community foundations in Manitoba service only settler-dominated cities and municipalities, with none on Native communities. As community foundations serve only their specific geographical areas, the community foundations in Manitoba effectively concentrate wealth in settler-dominated cities and municipalities, taking away needed resources from Native communities. In excluding the poorest communities in Manitoba, this philanthropic model further entrenches marginalization, poverty, and health risks for Native people on Native communities.
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The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.